{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gzip\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from Bio import SeqIO, SeqUtils\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-12-04 12:16:17--  ftp://ftp.vectorbase.org/public_data/organism_data/agambiae/Genome/agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz\n",
      "           => ‘gambiae.fa.gz’\n",
      "Resolving ftp.vectorbase.org (ftp.vectorbase.org)... 129.74.255.228\n",
      "Connecting to ftp.vectorbase.org (ftp.vectorbase.org)|129.74.255.228|:21... connected.\n",
      "Logging in as anonymous ... Logged in!\n",
      "==> SYST ... done.    ==> PWD ... done.\n",
      "==> TYPE I ... done.  ==> CWD (1) /public_data/organism_data/agambiae/Genome ... done.\n",
      "==> SIZE agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz ... 81591806\n",
      "==> PASV ... done.    ==> RETR agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz ... done.\n",
      "Length: 81591806 (78M) (unauthoritative)\n",
      "\n",
      "agambiae.CHROMOSOME 100%[===================>]  77.81M  3.01MB/s    in 26s     \n",
      "\n",
      "2020-12-04 12:16:44 (3.02 MB/s) - ‘gambiae.fa.gz’ saved [81591806]\n",
      "\n",
      "--2020-12-04 12:16:44--  https://vectorbase.org/common/downloads/Pre-VEuPathDB%20VectorBase%20files/Anopheles-atroparvus-EBRO_SCAFFOLDS_AatrE1.fa.gz\n",
      "Resolving vectorbase.org (vectorbase.org)... 128.192.21.13\n",
      "Connecting to vectorbase.org (vectorbase.org)|128.192.21.13|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 60723518 (58M) [application/x-gzip]\n",
      "Saving to: ‘atroparvus.fa.gz’\n",
      "\n",
      "atroparvus.fa.gz    100%[===================>]  57.91M  3.45MB/s    in 17s     \n",
      "\n",
      "2020-12-04 12:17:01 (3.40 MB/s) - ‘atroparvus.fa.gz’ saved [60723518/60723518]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!rm -f atroparvus.fa.gz gambiae.fa.gz 2>/dev/null\n",
    "!wget ftp://ftp.vectorbase.org/public_data/organism_data/agambiae/Genome/agambiae.CHROMOSOMES-PEST.AgamP3.fa.gz -O gambiae.fa.gz\n",
    "!wget https://vectorbase.org/common/downloads/Pre-VEuPathDB%20VectorBase%20files/Anopheles-atroparvus-EBRO_SCAFFOLDS_AatrE1.fa.gz -O atroparvus.fa.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gambiae_name = 'gambiae.fa.gz'\n",
    "atroparvus_name = 'atroparvus.fa.gz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chromosome:AgamP3:2L:1:49364325:1 chromosome 2L\n",
      "chromosome:AgamP3:2R:1:61545105:1 chromosome 2R\n",
      "chromosome:AgamP3:3L:1:41963435:1 chromosome 3L\n",
      "chromosome:AgamP3:3R:1:53200684:1 chromosome 3R\n",
      "chromosome:AgamP3:UNKN:1:42389979:1 chromosome UNKN\n",
      "chromosome:AgamP3:X:1:24393108:1 chromosome X\n",
      "chromosome:AgamP3:Y_unplaced:1:237045:1 chromosome Y_unplaced\n"
     ]
    }
   ],
   "source": [
    "recs = SeqIO.parse(gzip.open(gambiae_name, 'rt', encoding='utf-8'), 'fasta')\n",
    "for rec in recs:\n",
    "    print(rec.description)\n",
    "#Do not do this with atroparvus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "recs = SeqIO.parse(gzip.open(gambiae_name, 'rt', encoding='utf-8'), 'fasta')\n",
    "chrom_Ns = {}\n",
    "chrom_sizes = {}\n",
    "for rec in recs:\n",
    "    chrom = rec.description.split(':')[2]\n",
    "    if chrom in ['UNKN', 'Y_unplaced']:\n",
    "        continue\n",
    "    chrom_Ns[chrom] = []\n",
    "    on_N = False\n",
    "    curr_size = 0\n",
    "    for pos, nuc in enumerate(rec.seq):\n",
    "        if nuc in ['N', 'n']:\n",
    "            curr_size += 1\n",
    "            on_N = True\n",
    "        else:\n",
    "            if on_N:\n",
    "                chrom_Ns[chrom].append(curr_size)\n",
    "                curr_size = 0\n",
    "            on_N = False\n",
    "    if on_N:\n",
    "        chrom_Ns[chrom].append(curr_size)\n",
    "    chrom_sizes[chrom] = len(rec.seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2L (49364325): %Ns (1.7), num Ns: 957, max N: 28884\n",
      "2R (61545105): %Ns (2.3), num Ns: 1658, max N: 36427\n",
      "3L (41963435): %Ns (2.9), num Ns: 1272, max N: 31063\n",
      "3R (53200684): %Ns (1.8), num Ns: 1128, max N: 24292\n",
      "X (24393108): %Ns (4.1), num Ns: 1287, max N: 21132\n"
     ]
    }
   ],
   "source": [
    "for chrom, Ns in chrom_Ns.items():\n",
    "    size = chrom_sizes[chrom]\n",
    "    print('%s (%s): %%Ns (%.1f), num Ns: %d, max N: %d' % (chrom, size, 100 * sum(Ns) / size, len(Ns), max(Ns)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Atroparvus super-contigs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "recs = SeqIO.parse(gzip.open(atroparvus_name, 'rt', encoding='utf-8'), 'fasta')\n",
    "sizes = []\n",
    "size_N = []\n",
    "for rec in recs:\n",
    "    size = len(rec.seq)\n",
    "    sizes.append(size)\n",
    "    count_N = 0\n",
    "    for nuc in rec.seq:\n",
    "        if nuc in ['n', 'N']:\n",
    "            count_N += 1\n",
    "    size_N.append((size, count_N / size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1371 8304.0 163596.0065645514 20238125 1004 1563.0 56612.0\n"
     ]
    }
   ],
   "source": [
    "print(len(sizes), np.median(sizes), np.mean(sizes), max(sizes), min(sizes),\n",
    "      np.percentile(sizes, 10), np.percentile(sizes, 90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Fraction of Ns per contig size')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "small_split = 4800\n",
    "large_split = 540000\n",
    "fig, axs = plt.subplots(1, 3, figsize=(16, 9), squeeze=False, sharey=True)\n",
    "xs, ys = zip(*[(x, 100 * y) for x, y in size_N if x <= small_split])\n",
    "axs[0, 0].plot(xs, ys, '.')\n",
    "xs, ys = zip(*[(x, 100 * y) for x, y in size_N if x > small_split and x <= large_split])\n",
    "axs[0, 1].plot(xs, ys, '.')\n",
    "axs[0, 1].set_xlim(small_split, large_split)\n",
    "xs, ys = zip(*[(x, 100 * y) for x, y in size_N if x > large_split])\n",
    "axs[0, 2].plot(xs, ys, '.')\n",
    "axs[0, 0].set_ylabel('Fraction of Ns', fontsize=12)\n",
    "axs[0, 1].set_xlabel('Contig size', fontsize=12)\n",
    "fig.suptitle('Fraction of Ns per contig size', fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
